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Activity Number: 569
Type: Contributed
Date/Time: Thursday, August 6, 2009 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #304771
Title: Multiple Imputation for Drop-Out in Longitudinal Studies
Author(s): Xiaodong Li and Jingchen Liu*+ and Naihua Duan and Huiping Jiang and Jeffrey Lieberman and Ragy Girgis
Companies: Columbia University and Columbia University and Columbia University and Columbia University and Columbia University and Columbia University
Address: 1255 Amsterdam Ave, New York, NY, 10027,
Keywords:
Abstract:

Missing data are ubiquitous in longitudinal studies. In this paper, we propose an imputation model to handle ignorable drop-outs in longitudinal studies. Under the monotone missing pattern, the imputation task can be carried out sequentially. Our model allows the missing observations to depend on not only the immediate previous state, but also the entire history of the same participant. Furthermore, the built-in model selection mechanism chooses between parametric model and nonparametric model to impute each missing observation. Unlike the classic model selection problems which aim to find one model that fits the entire data set well, our choices of models are allowed to be different to impute each missing observations, that is, to find the best model to predict each missing observation.

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